In Table REF , we present the AUC, FAR to compare our MIST with related state-of-the-art [rgb]0,0,0online methods in terms of accuracy and robustness. We can find that MIST outperforms or performs similarly to all other methods in terms of all evaluation metrics from Table REF , which confirms the efficacy of MIST.
Specifically, the results of Zhong et al. [1]}, marked with \(*\) , are re-tested from the official released modelshttps://github.com/jx-zhong-for-academic-purpose/GCN-Anomaly-Detection. without deploying 10-crop10-crop is a test-time augmentation of cropping images into the center, four corners and their mirrored counterparts. for fair comparison, while the results in brackets are reported on [1]} using 10-crop augmentation. However, 10-crop augmentation may improve the performance but requires 10 times the computation.
Notably, the result of our MIST still slightly overtakes that of Zhong et al. [1]} using 10-crop augmentation (\(81.08\%\)  vs. \(81.40\%\)  in terms of AUC and \(2.2\%\)  vs. \(2.19\%\)  for FAR). Moreover, our method outperforms the supervised method of Liu et al. [4]}, which trains \(C3D^{RGB}\)  with external temporal annotations and \(NLN^{RGB}\)  with external spatiotemporal annotations.
These results verify that our proposed MIST is more effective than previous works.
